用预测+启发式方法，让云资源又省钱又响应快。
Intelligent Cloud Orchestration: A Hybrid Predictive and Heuristic Framework for Cost Optimization
- 结合LSTM预测与博弈论启发式调度
- 成本接近纯预测模型，响应速度如启发式方法
- 适合需要兼顾成本与实时性的云管理场景
云计算支持弹性资源调配，但动态工作负载常导致因过度配置而产生更高成本。基于机器学习（如长短期记忆网络，LSTM）的方法在高层预测工作负载模式方面有效，但在突发流量高峰时可能引入延迟。相反，数学启发式方法（如博弈论）能快速可靠地做出调度决策，但无法考虑未来工作负载变化。为解决这一权衡，本文提出一种混合编排框架，将基于LSTM的预测扩容与启发式任务分配相结合。结果表明，该方法在保持类似启发式方法的快速响应时间的同时，使基础设施成本接近纯机器学习模型水平。本工作为提升云资源管理的成本效率提供了实用方案。
原文摘要 · Abstract (English)
Cloud computing allows scalable resource provisioning, but dynamic workload changes often lead to higher costs due to over-provisioning. Machine learning (ML) approaches, such as Long Short-Term Memory (LSTM) networks, are effective for predicting workload patterns at a higher level, but they can introduce delays during sudden traffic spikes. In contrast, mathematical heuristics like Game Theory provide fast and reliable scheduling decisions, but they do not account for future workload changes. To address this trade-off, this paper proposes a hybrid orchestration framework that combines LSTM-based predictive scaling with heuristic task allocation. The results show that this approach reduces infrastructure costs close to ML-based models while maintaining fast response times similar to heuristic methods. This work presents a practical approach for improving cost efficiency in cloud resource management.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。